The use of multi-user multiple-input multiple-output orthogonal frequency division multiplexing (MU-MIMO-OFDM) has been considered for next-generation wireless systems. FIG. 1 shows an example of MU-MIMO-OFDM system where the central access point (AP), equipped with NAP antennas, communicates with NUT user terminals (UTs), each equipped with a single antenna. Wireless OFDM links between the AP and the UTs can be established at the same time using the same radio frequency, improving the spectrum efficiency of the system, ideally, by the minimum of NAP and NUT, compared to a conventional single-transmitter-single-receiver system. This mode of operation is also known as space division multiple access (SDMA). Uplink MU-MIMO-OFDM transmission (from UTs to AP) can be realised by the AP receiver having an accurate knowledge of the uplink channel. Downlink MU-MIMO-OFDM transmission (from AP to UTs) can be realised by the AP transmitter having an accurate knowledge of the downlink channel.
One of the challenges to realise a successful implementation of a MU-MIMO-OFDM system achieving higher spectrum efficiency is to have an efficient and accurate channel estimation method. Basically, channel estimation errors degrade achievable signal to noise plus interference ratio (SNIR), both for uplink and downlink, which in turn degrades achievable data transfer rates.
Accurate estimation of OFDM channel is typically performed by sending symbols known by the receiver in advance. Such symbols known by the receiver in advance for the purpose of channel estimation are referred as channel training symbols. The longer the time the system spends sending known symbols for channel estimation, the shorter the time the system can spend for sending information. The time that the system spends for channel estimation is referred as channel training overheads. Typically, the channel training overheads increases with the number of transmitters, as the estimation of the channel is required for each active transmitter. A method for efficiently and accurately estimating channels in the presence of a large number of transmitters is called for.
One known method for performing channel estimation is to use comb-pilot sub-carriers with phase slope estimation in the presence of OFDM frame timing offset. Generally, conventional channel estimation methods that use comb-pilot sub-carriers estimate the channel coefficients at comb-pilot sub-carriers and then compute the channel coefficients of the remaining sub-carriers by interpolating the estimated channel coefficients of the comb-pilot sub-carriers.
As shown in FIG. 2, the conventional comb-pilot sub-carrier arrangement places comb-pilot sub-carriers uniformly along the frequency. This example shows an interpolation factor of 4 with equal spacing between comb-pilot sub-carriers. This conventional equally spaced arrangement was previously recommended in the absence of prior knowledge about the channel. This conventional equally spaced arrangement is also known to be beneficial in utilising certain types of interpolation methods, such as transform-domain processing. Previously, the phase slope of the channel coefficients along the frequency was estimated by the estimated channel coefficients of equally spaced comb-pilot sub-carriers, which inevitably has a larger variation due to the larger spacing of the comb-pilot sub-carriers.
The spectrum efficiency of an OFDM communication link improves by using less sub-carriers for pilot and more sub-carriers for transmitting data. The ratio of the number of comb-pilot sub-carriers over the number of all sub-carriers for which the channel coefficients are calculated by the interpolation is known as the interpolation factor. There is a trade-off between achieving higher spectrum efficiency and degradation in channel estimation accuracy by using a larger interpolation factor.
In practice OFDM frame timing offset causes fast phase changes in estimated channel coefficients of the comb-pilot sub-carriers, and results in significant error in estimating channel coefficients at remaining sub-carriers by interpolation. Previously proposed methods were found to work well up to the OFDM frame timing offset in terms of N/(2 F) baseband samples, where N is the number of baseband samples per OFDM frame and F is the interpolation factor.
However, currently there is no known method that can allow the OFDM frame timing offset larger than N/(2 F) without ambiguity.
Another of the challenges is to have an efficient and accurate channel state feedback method for a MU-MIMO-OFDM system. Generally, the access point is equipped with multiple transmitters communicating with multiple user terminals each having one or more receiving antennas. With the access point knowing the downlink channel coefficients, the spectrum efficiency of the multi-user MIMO downlink can increase linearly with the number of transmitters at the access point, provided sufficient number of users are present in the system.
In order to perform downlink MU-MIMO-OFDM, the access point transmitters need to know the downlink channel. The access point can identify the downlink channel by 1) estimating the downlink channel at each user terminal by sending channel training symbols in a first time period, and 2) receiving the estimated downlink channel information from each user terminal in a second time period. The longer the MU-MIMO-OFDM system spends sending estimated downlink channel information, the shorter the time the MU-MIMO-OFDM system can spend sending information. The time that system spends feeding back estimated channel information is referred as channel feedback overheads. Channel feedback overheads may be reduced if channel information is compressed with some loss of information, referred to as channel compression. Typically, the time required for the system to feedback the channel information increases with the number of transmitter, as the channel information is required for each active transmitter.
A number of well-known channel state feedback schemes have been previously investigated including analog feedback and vector quantisation. It has been concluded from these investigations that the most suitable method in terms of simplicity and overhead minimisation for multi-user MIMO-OFDM is to use time-domain quantisation. This has therefore lead to the use of discrete Fourier transform (DFT) based channel compression for the purposes of efficient channel feedback in multi-user MIMO-OFDM communication. DFT based channel compression has been shown to theoretically outperform other known channel compression methods. The use of DFT to transform frequency domain channel coefficients into time domain impulse response coefficients and the use of scalar uniform quantisation with some (unspecified) bit-allocation scheme was suggested to quantise the time domain impulse response real and imaginary coefficients. The method was shown to achieve better sum rates over previous methods analytically.
Anther particular challenge to realise a successful implementation of a MU-MIMO-OFDM systems is to reduce the required computational complexity. Zero-forcing detection and zero-forcing pre-coding are known as low complexity linear receiver and pre-coder for MU-MIMO-OFDM uplink and downlink. However, even with these linear low-complexity methods, the computational complexity required for zero-forcing based uplink and downlink MU-MIMO-OFDM can increase dramatically, as both methods involve inversion of a channel matrix, wherein the matrix size can become large with a large number of transmitters.
Interpolation of channel inverse in point-to-point MIMO-OFDM communication was proposed previously. In particular, the interpolation of channel inverse is achieved by performing interpolation separately of the adjoint and determinant of the channel matrix. This is expressed as follows:
            H              -        1              ⁡          (              i        F            )        =            adj      ⁢                          ⁢              H        ⁡                  (                      i            F                    )                            det      ⁢                          ⁢              H        ⁡                  (                      i            F                    )                    where H is the channel matrix. It was concluded that interpolating the channel matrix from a smaller set of inverses will, in general, not be possible.
A method for interpolation-based QR decomposition in MIMO-OFDM systems has also previously been proposed. This method is applicable to point-to-point MIMO channels as well as point-to-multipoint multi-user MIMO channel that requires per-tone QR decomposition. In this method, the interpolation is performed separately on Q (i.e. the orthogonal matrix) and R (i.e. the upper triangular matrix) matrices of the channel matrix.
There is therefore a need to alleviate one or more of the above mentioned challenges or provide a useful alternative.
The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as, an acknowledgement or admission or any form of suggestion that that prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.